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Classification of Serranidae Species Using Color Based Statistical Features

Bilal İşçimen*, Yakup Kutlu, Cemal Turan

Abstract

In this study 6 species (Epinephelus aeneus, Epinephelus caninus, Epinephelus costae, Epinephelus marginatus, Hyporthodus haifensis, Mycteroperca rubra) of Serranidae family were classified by using a color based feature extraction method. A database which consists of 112 fish images was used in this study. In each image, a fish was located on a white background floor with the same position and the images were taken from different distances. A combination of manual processes and automatic algorithms were applied on images until obtaining colored fish sample images with a black background. Since the presented color based feature extraction method avoids including background, these images were processed by using an automatic algorithm in order to obtain a solid texture image from the fish and extract features. The obtained solid texture image was in HSV color space and used due to extract meaningful information about fish sample. Each of the hue, saturation and value components of the HSV color space was used separately in order to extract 7 statistical features. Hence, totally 21 features were extracted for each fish sample. The extracted features were used within Nearest Neighbor algorithm and 112 fish samples from 6 species were classified with an overall accuracy achievement of 86%.

Keywords

Classification, Serranidae family, HSV, color, texture

 

Volume 2, No 1, 25-34, 2017

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References
  • Alsmadi, M. K., Omar, K. B., Noah, S.A., Almarashdeh, I. (2010). Fish recognition based on robust features extraction from size and shape measurements using neural network. Journal of Computer Science, 6 (10): 1088.
  • Alsmadi, M. K., Omar, K.B., Noah, S.A., Almarashdeh, I. (2010). Fish recognition based on robust features extraction from color texture measurements using back-propagation classifier. Journal of Theoritical and Applied Information Technology.
  • Atasoy, H., Yildirim, E., Kutlu, Y., Tohma, K. (2015). Webcam Based Real-Time Robust Optical Mark Recognition. International Conference on Neural Information Processing. Springer International Publishing, pp. 449-456.
  • Benson, B., Cho, J., Goshorn, D., Kastner, R. (2009). Field programmable gate array (FPGA) based fish detection using Haar classifiers. American Academy of Underwater Sciences.
  • Bothmann, L., Windmann, M., Kauermann, G. (2016). Realtime classification of fish in underwater sonar videos. Journal of the Royal Statistical Society: Series C (Applied Statistics).
  • Cabreira, A.G., Tripode, M., Madirolas, A. (2009). Artificial neural networks for fish-species identification. ICES Journal of Marine Science: Journal du Conseil.
  • Chuang, M. C., Hwang, J. N., Williams, K. (2016). A Feature Learning and Object Recognition Framework for Underwater Fish Images. IEEE Transactions on Image Processing, 25(4): 1862-1872.
  • Chuang, M.C., Hwang, J.N., Kuo, F.F., Shan, M.K., Williams, K. (2014). Recognizing live fish species by hierarchical partial classification based on the exponential benefit. IEEE International Conference on Image Processing (ICIP), pp. 5232-5236.
  • Cunningham, P., Delany, S.J. (2007). k-Nearest neighbour classifiers.Multiple Classifier Systems, 1-17.
  • D’Elia, M., Patti, B., Bonanno, A., Fontana, I., Giacalone, G., Basilone, G., Fernandes, P.G. (2014). Analysis of backscatter properties and application of classification procedures for the identification of small pelagic fish species in the Central Mediterranean. Fisheries Research, 149, 33-42.
  • Daramola, S.A., Omololu, O. (2016). Fish Classification Algorithm using Single Value Decomposition. International Journal of Innovative Research in Science, Engineering and Technology, 5 (2): 1621-1626.
  • Fabic, J. N., Turla, I. E., Capacillo, J.A., David, L.T., Naval, P.C. (2013). Fish population estimation and species classification from underwater video sequences using blob counting and shape analysis. Underwater Technology Symposium (UT), IEEE International, pp. 1-6.
  • Forsyth, D.A. and Ponce, J. (2002). Computer vision: a modern approach, Prentice Hall Professional Technical Reference, ch. 15.
  • Hu, J., Li, D., Duan, Q., Han, Y., Chen, G., Si, X. (2012). Fish species classification by color, texture and multi-class support vector machine using computer vision. Computers and Electronics in Agriculture, 88, 133-140.
  • Huang, P.X., Boom, B.J., Fisher, R.B. (2015). Hierarchical classification with reject option for live fish recognition. Machine Vision and Applications, 26 (1): 89-102.
  • Iscimen, B., Atasoy, H., Kutlu, Y., Yildirim, S., Yildirim, E. (2015). Smart Robot Arm Motion Using Computer Vision. Elektronika ir Elektrotechnika, 21(6): 3-7.
  • Iscimen, B., Kutlu, Y., Reyhaniye, A. N., Turan, C. (2014). Image analysis methods on fish recognition. 22nd Signal Processing and Communications Applications Conference (SIU), pp. 1411-1414.
  • Iscimen, B., Kutlu, Y., Uyan, A., Turan, C. (2015). Classification of fish species with two dorsal fins using centroid-contour distance. 23nd Signal Processing and Communications Applications Conference (SIU), pp. 1981-1984.
  • Manzoor, S., Islam, R. U., Khalid, A., Samad, A., Iqbal, J. (2014). An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation. Robotics and Computer-Integrated Manufacturing, 30 (3): 351-362.
  • Mizuno, K., Liu, X., Asada, A., Ashizawa, J., Fujimoto, Y., Shimada, T. (2015). Application of a high-resolution acoustic video camera to fish classification: An experimental study. Underwater Technology (UT), IEEE, pp. 1-4.
  • Ogunlana, S.O., Olabode, O., Oluwadare, S.A.A., Iwasokun, G.B. (2015). Fish Classification Using Support Vector Machine. African Journal of Computing & ICT, 8(2): 75-82.
  • Shafait, F., Mian, A., Shortis, M., Ghanem, B., Culverhouse, P. F., Edgington, D., Cline, D., Ravanbakhsh, M., Seager, J., Harvey, E. S. (2016). Fish identification from videos captured in uncontrolled underwater environments. ICES Journal of Marine Science: Journal du Conseil, 73 (10): 2737-2746.
  • Turan, C., Erguden, D., Gürlek, M., Yaglioglu, D., Keskin, Ç. (2007). Atlas and systematics of marine Bony fishes of Turkey. Nobel, Adana, Turkey.